Large language models (LLMs) are able to engage in natural-sounding conversations with humans, showcasing unprecedented capabilities for information retrieval and automated decision support. They have disrupted human–technology interaction and the way businesses operate. However, technologies based on generative artificial intelligence are known to hallucinate, misinform, and display biases introduced by the massive datasets on which they are trained. Existing research indicates that humans may unconsciously internalize these biases, which can persist even after they stop using the programs. In this study, the authors explore the cultural self-perception of LLMs by prompting ChatGPT (OpenAI) and Bard (Google) with value questions derived from the GLOBE (Global Leadership and Organizational Behavior Effectiveness) project. The findings reveal that LLMs’ cultural self-perception is most closely aligned with the values of English-speaking countries and countries characterized by economic competitiveness. It is crucial for all members of society to understand how LLMs function and to recognize their potential biases. If left unchecked, the “black-box” nature of AI could reinforce human biases, leading to the inadvertent creation and training of even more biased models.
AbdulhaiMarwaSerapio-GarciaGregoryCrepyClémentValterDariaCannyJohnJaquesNatasha (2023), “Moral Foundations of Large Language Models,” arXiv, https://doi.org/10.48550/arXiv.2310.15337.
2.
AbidAbubakarFarooqiMaheenZouJames (2021), “Persistent Anti-Muslim Bias in Large Language Models,” in AIES ’21: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. Association for Computing Machinery, 298–306.
3.
AcerbiAlbertoStubbersfieldJoseph M. (2023), “Large Language Models Show Human-Like Content Biases in Transmission Chain Experiments,” PNAS, 120 (44), e2313790120.
4.
Afgiansyah (2023), “Artificial Intelligence Neutrality: Framing Analysis of GPT Powered-Bing Chat and Google Bard,” Jurnal Riset Komunikasi, 6 (2), 179–93.
5.
AlesinaAlbertoDevleeschauwerArnaudEasterlyWilliamKurlatSergioWacziargRomain (2003), “Fractionalization,” Journal of Economic Growth, 8 (2), 155–94.
BerryHeatherGuillénMauro F.ZhouNan (2010), “An Institutional Approach to Cross-National Distance,” Journal of International Business Studies, 41 (9), 1460–80.
8.
BeugelsdijkSjoerdMaselandRobbertOnrustMarjolijnVan HoornAndréSlangenArjen (2015), “Cultural Distance in International Business and Management: From Mean-Based to Variance-Based Measures,” International Journal of Human Resource Management, 26 (2), 165–91.
9.
BrownTom B.MannBenjaminRyderNickSubbiahMelanieKaplanJaredDhariwalPrafullaNeelakantanArvindShyamPranavSastryGirishAskellAmandaAgarwalSandhiniHerbert-VossArielKruegerGretchenHenighanTomChildRewonRameshAdityaZieglerDaniel M.WuJeffreyWinterClemensHesseChristopher, et al. (2020), “Language Models Are Few-Shot Learners,” in Advances in Neural Information Processing Systems, Proceedings of the 34th International Conference on Neural Information Processing Systems, 1877–901, https://dl.acm.org/doi/abs/10.5555/3495724.3495883.
10.
BubeckSébastienChandrasekaranVarunEldanRonenGehrkeJohannesHorvitzEricKamarEceLeePeterLeeYin TatLiYuanzhiLundbergScottNoriHarshaPalangiHamidTulio RibeiroMarcoZhangYi (2023), “Sparks of Artificial General Intelligence: Early Experiments with GPT-4,” arXiv, https://doi.org/10.48550/arXiv.2303.12712.
11.
BudhwarPawanChowdhurySoumyadebWoodGeoffreyAguinisHermanBamberGreg J.BeltranJose R.BoseliePaulCookeFang LeeDeckerStephanieDeNisiAngeloKumar DeyPrasantaGuestDavidKnoblichAndrew J.MalikAshishPaauweJaapPapagiannidisSavvasPatelCharmiPereiraVijayRenShuangRogelbergSteven, et al. (2023), “Human Resource Management in the Age of Generative Artificial Intelligence: Perspectives and Research Directions on ChatGPT,” Human Resource Management Journal, 33 (3), 606–59.
12.
CaoYongZhouLiLeeSeolhwaCabelloLauraChenMinHershcovichDaniel (2023), “Assessing Cross-Cultural Alignment Between ChatGPT and Human Societies: An Empirical Study,” arXiv, https://doi.org/10.48550/arXiv.2303.17466.
13.
ChenYangKirshnerSamuel N.OvchinnikovAntonAndiappanMeenaJenkinTracy (2025), “A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do?” Manufacturing & Service Operations Management (published online January 31, 2025), https://doi.org/10.1287/msom.2023.0279.
14.
ChengJianpengDongLiLapataMirella (2016), “Long Short-Term Memory-Networks for Machine Reading,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 551–61.
15.
ChowdheryAakankshaNarangSharanDevlinJacob (2023), “PaLM: Scaling Language Modeling with Pathways,” Journal of Machine Learning Research, 24 (240), 1–113.
DeleersnyderBarbaraDekimpeMarnik G.SteenkampJan-Benedict E.M.LeeflangPeter S.H. (2009), “The Role of National Culture in Advertising’s Sensitivity to Business Cycles: An Investigation Across Continents,” Journal of Marketing Research, 46 (5), 623–36.
18.
EggerPeter H.ToubalFarid (2016), “Common Spoken Languages and International Trade,” in The Palgrave Handbook of Economics and Language, GinsburghVictorWeberShlomo, eds. Springer, 263–89.
FieldJames G.BoscoFrank A.KraichyDavidUggerslevKrista L.GeigerMingang K. (2021), “More Alike Than Different? A Comparison of Variance Explained by Cross-Cultural Models,” Journal of International Business Studies, 52 (9), 1797–817.
22.
FrankKenneth A. (2000), “Impact of a Confounding variable on a Regression Coefficient,” Sociological Methods and Research, 29 (2), 147–94.
23.
FujimotoSasukeTakemotoKazuhiro (2023), “Revisiting the Political Biases of ChatGPT,” Frontiers in Artificial Intelligence, 6, 1232003.
24.
GeletkanyczMarta A. (1997), “The Salience of ‘Culture’s Consequences’: The Effects of Cultural Values on Top Executive Commitment to the Status Quo,” Strategic Management Journal, 18 (8), 615–34.
25.
GhafouriVahidAgarwalVibhorZhangYongSastryNishanthSuchJoseSuarez-TangilGuillermo (2023), “AI in the Gray: Exploring Moderation Policies in Dialogic Large Language Models vs. Human Answers in Controversial Topics,” in CIKM ’23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, 556–65.
GoldfarbBrentKirschDavid A. (2019), Bubbles and Crashes. Stanford University Press.
29.
GoodfellowIanBengioYoshuaCourvilleAaron (2016), Deep Learning. MIT Press.
30.
HangesPaul J.DicksonMarcus W. (2006), “Agitation over Aggregation: Clarifying the Development of and the Nature of the GLOBE Scales,” Leadership Quarterly, 17 (5), 522–36.
31.
HanifiMasihChibaneHichamHoussinRemyCavallucciDenis (2022), “Problem Formulation in Inventive Design Using Doc2vec and Cosine Similarity as Artificial Intelligence Methods and Scientific Papers,” Engineering Applications of Artificial Intelligence, 109, 104661.
32.
HarrerStefan (2023), “Attention Is Not All You Need: The Complicated Case of Ethically Using Large Language Models in Healthcare and Medicine,” eBioMedicine, 90, 104512.
33.
HartmannJochenSchwenzowJasperWitteMaximilian (2023), “The Political Ideology of Conversational AI: Converging Evidence on ChatGPT’s Pro-Environmental, Left-Libertarian Orientation,” SSRN, https://doi.org/10.2139/ssrn.4316084.
34.
HofstedeGeert (1980), Culture’s Consequences: International Differences in Work-Related Values. Sage.
35.
HofstedeGeertHofstedeGert JanMinkovMichael (2010), Cultures and Organizations: Software of the Mind. McGraw-Hill.
36.
HopkinsAndrew (2006), “Studying Organisational Cultures and Their Effects on Safety,” Safety Science, 44 (10), 875–89.
37.
HouseRobert J.JavidanMansour (2004), “Overview of GLOBE,” in Culture, Leadership, and Organizations: The GLOBE Study of 62 Societies, HouseRobert J.HangesPaul J.JavidanMansourDorfmanPeter W.GuptaVipin, eds. Sage, 9–28.
38.
HuangJieChangKevin Chen-Chuan (2023), “Citation: A Key to Building Responsible and Accountable Large Language Models,” arXiv, https://doi.org/10.48550/arXiv.2307.02185.
39.
HuangYufeiXiongDeyi (2023), “CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Models,” arXiv, https://doi.org/10.48550/arXiv.2306.16244.
40.
JargowskyPaul A (2005), “The Ecological Fallacy,” in The Encyclopedia of Social Measurement, Kempf-LeonardKimberly, ed. Elsevier, 771–805.
41.
JiaoQiluZhangShunyao (2021), “A Brief Survey of Word Embedding and Its Recent Development,” in IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference. IEEE, 1697–701.
42.
JohnsonRebecca L.PistilliGiadaMenédez-GonzálezNataliaDuranLeslye Denisse DiasPanaiEnricoKalpokieneJulijaBertulfoDonald Jay (2022), “The Ghost in the Machine Has an American Accent: Value Conflict in GPT-3,” arXiv, https://doi.org/10.48550/arXiv.2203.07785.
43.
KasirzadehAtoosaGabrielIason (2023), “In Conversation with Artificial Intelligence: Aligning Language Models with Human Values,” Philosophy and Technology, 36, 27.
44.
KhyarehMohsen MohammadiRostamiNasrin (2022), “Macroeconomic Conditions, Innovation and Competitiveness,” Journal of the Knowledge Economy, 13, 1321–40.
45.
KimYoungseekStantonJeffrey M (2016), “Institutional and Individual Factors Affecting Scientists’ Data-Sharing Behaviors: A Multilevel Analysis,” Journal of the Association for Information Science and Technology, 67 (4), 776–99.
46.
KogutBruceSinghHarbir (1988), “The Effect of National Culture on the Choice of Entry Mode,” Journal of International Business Studies, 19 (3), 411–32.
47.
KonaraPalithaMohrAlexander (2019), “Why We Should Stop Using the Kogut and Singh Index,” Management International Review, 59 (3), 335–54.
48.
KshetriNirDwivediYogesh K.DavenportThomas H.PanteliNiki (2024), “Generative Artificial Intelligence in Marketing: Applications, Opportunities, Challenges, and Research Agenda,” International Journal of Information Management, 75, 102716.
49.
LasaponaraRosaMasiniNicola (2012), “Pattern Recognition and Classification Using VHR Data for Archaeological Research,” in Satellite Remote Sensing: A New Tool for Archaeology, LasaponaraRosaMasiniNicola, eds. Springer, 65–86.
LiangPaul PuLiIrene MengzeZhengEmilyLimYao ChongSalakhutdinovRuslanMorencyLouis Philippe (2020), “Towards Debiasing Sentence Representations,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. National Science Foundation, 5502–15.
53.
LintonDavid (1992), “The Luddites: How Did They Get That Bad Reputation?” Labor History, 33 (4), 529–37.
54.
LiuRuiboJiaChenyanWeiJasonXuGuangxuanVosoughiSoroush (2022a), “Quantifying and Alleviating Political Bias in Language Models,” Artificial Intelligence, 304, 103654.
55.
LiuDungangLiShaoboYuYanMoustakiIrini (2021), “Assessing Partial Association Between Ordinal Variables: Quantification, Visualization, and Hypothesis Testing,” Journal of the American Statistical Association, 116 (534), 955–68.
56.
LiuRuiboZhangGeFengXinyuVosoughiSoroush (2022b), “Aligning Generative Language Models with Human Values,” in Findings of the Association for Computational Linguistics: NAACL 2022. Association for Computational Linguistics, 242–53.
57.
LucyLiBammanDavid (2021), “Gender and Representation Bias in GPT-3 Generated Stories,” in Proceedings of the Third Workshop on Narrative Understanding. Association for Computational Linguistics, 48–55.
58.
MageeLiamGhahremanlouLidaSoldaticKarenRobertsonShanthi (2021), “Intersectional Bias in Causal Language Models,” arXiv, https://doi.org/10.48550/arXiv.2107.07691.
59.
ManyikaJamesHsiaoSissie (2023), “An Overview of Bard: An Early Experiment with Generative AI,” Google (accessed November 15, 2023), https://ai.google/static/documents/google-about-bard.pdf, 1–9.
60.
MaranoValentinaSauerwaldSteveVan EssenMarc (2022), “The Influence of Culture on the Relationship Between Women Directors and Corporate Social Performance,” Journal of International Business Studies, 53 (7), 1315–42.
61.
MasoudReem LLiuZiquanFeriancMartinTreleavenPhilipRodriguesMiguel (2023), “Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede’s Cultural Dimensions,” arXiv, https://doi.org/10.48550/arXiv.2309.12342.
MessnerWolfgang (2021), “Geometrical Measurement of Cultural Differences,” Journal of International Marketing, 29 (3), 43–62.
64.
MessnerWolfgang (2023a), “Being Happy: The Role of Personal Value Priorities in Subjective Well-Being Across European Countries,” International Journal of Cross Cultural Management, 23 (2), 389–421.
65.
MessnerWolfgang (2023b), “From Black Box to Clear Box: A Hypothesis Testing Framework for Scalar Regression Problems Using Deep Artificial Neural Networks,” Applied Soft Computing, 146, 110729.
66.
MessnerWolfgang (2024a), “Distance Is the Spice, but Not the Whole Enchilada: Country-Pair Psychic Distance Stimuli and Country Fixed Effects in a Deep Learning Implementation of the Trade Flow Model,” International Business Review, 33 (1), 102201.
67.
MessnerWolfgang (2024b), “Exploring Multilevel Data with Deep Learning and XAI: The Effect of Personal-Care Advertising Spending on Subjective Happiness,” International Business Review, 33 (1), 102203.
MotokiFabioNetoValdemar PinhoRodriguesVictor (2023), “More Human Than Human: Measuring ChatGPT Political Bias,” Public Choice, 1–20.
70.
MuralidharDeepa (2021), “Examining Religion Bias in AI Text Generators,” in Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES), FourcadeMarionKuipersBenjaminLazarSethMulliganDeirde, eds. Association for Computing Machinery, 273–74.
NadeemMoinBethkeAnnaReddySiva (2021), “StereoSet: Measuring Stereotypical Bias in Pretrained Language Models,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ZongChengquingXiaFeiLiWenjieNavigliRoberto, eds. Association for Computational Linguistics, 5356–71.
73.
NajafabadiMaryam M.VillanustreFlavioKhoshgoftaarTaghi M.SeliyaNaeemWaldRandallMuharemagicEdin (2015), “Deep Learning Applications and Challenges in Big Data Analytics,” Journal of Big Data, 2, 1.
74.
NaousTarekRyanMichael J.XuWei (2023), “Having Beer After Prayer? Measuring Cultural Bias in Large Language Models,” arXiv, https://doi.org/10.48550/arXiv.2305.14456.
PetersonMark FBarretoTais Siqueira (2014), “The Like It or Not Proposition: Implications of Societal Characteristics for the Cultural Expertise and Personal Values of Organization Members,” Journal of Organizational Behavior, 35 (8), 1134–52.
77.
PetersonMark F.SøndergaardMikael (2011), “Traditions and Transitions in Quantitative Societal Culture Research in Organization Studies,” Organization Studies, 32 (11), 1539–58.
78.
PiresTelmoSchlingerEvaGarretteDan (2019), “How Multilingual Is Multilingual BERT?” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 4996–5001.
79.
PolsonNicholas G.SokolovVadim O. (2017), “Deep Learning for Short-Term Traffic Flow Prediction,” Transportation Research Part C: Emerging Technologies, 79, 1–17.
80.
RauchJames E. (1999), “Networks Versus Markets in International Trade,” Journal of International Economics, 48 (1), 7–35.
81.
RavfogelShauliElazarYanaiGonenHilaTwitonMichaelGoldbergYoav (2020), “Null it out: Guarding Protected Attributes by Iterative Nullspace Projection,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, JurafskyDanChaiJoyceSchluterNatalieTetreaultJoel, eds. Association for Computational Linguistics, 7237–56.
82.
RosenbergJoshua M.NarvaizSarahXuRanLinQinyunMaroulisSpiroFrankKen A. (2023), “Konfound-It! Quantify the Robustness of Causal Inferences (V2.0.0),” (accessed November 20, 2023), https://konfound-project.shinyapps.io/konfound-it/.
83.
RuderSebastianVulićIvanSøgaardAnders (2019), “A Survey of Cross-Lingual Word Embedding Models,” Journal of Artificial Intelligence Research, 65, 569–630.
84.
RutinowskiJérômeFrankeSvenEndendykJanDormuthInaPaulyMarkus (2023), “The Self-Perception and Political Biases of ChatGPT,” arXiv, https://doi.org/10.48550/arXiv.2304.07333.
85.
SamekWojciechMontavonGrégoireLapuschkinSebastianAndersChristopher J.MüllerKlaus Robert (2021), “Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications,” Proceedings of the IEEE, 109 (3), 247–78.
86.
SchlösserOliverFreseMichaelHeintzeAnna MariaAl-NajjarMusaedArciszewskiThomasBesevegisEliasBishopGeorge D.BonnesMiriliaCleggChris W.Drozda-SenkowskaEwaGaboritMauricioGarzonDayraHansenTia G.B.HeszenIrenaJuhaszMartaKeatingMary A.MangundjayaWustariMansorNormaMitchelsonJacqueline K.Ortiz-ReynosoAlejandra, et al. (2013), “Humane Orientation as a New Cultural Dimension of the GLOBE Project: A Validation Study of the GLOBE Scale and Out-Group Humane Orientation in 25 Countries,” Journal of Cross-Cultural Psychology, 44 (4), 535–51.
SchwabKlausSala-i-MartínXavier (2017), “The Global Competitiveness Report 2017–2018,” World Economic Forum (accessed July 30, 2022), https://www3.weforum.org/docs/GCR2017-2018/05FullReport/TheGlobalCompetitivenessReport2017%E2%80%932018.pdf.
89.
SejnowskiTerrence J. (2023), “Large Language Models and the Reverse Turing Test,” Neural Computation, 35 (3), 309–42.
90.
ShihadehJulianaAckermanMargaretaTroskeAshleyLawsonNicoleGonzalezEdith (2022), “Brilliance Bias in GPT-3,” in 2022 IEEE Global Humanitarian Technology Conference (GHTC). IEEE, 62–69.
SuriGauravSlaterLilyZiaeeAliNguyenMorgan (2023), “Do Large Language Models Show Decision Heuristics Similar to Humans? A Case Study Using GPT-3.5,” SSRN, https://doi.org/10.2139/ssrn.4440608.
94.
TerlutterRalf, eds. Deutscher Universitätsverlag, 419–38.
95.
TerlutterRalfDiehlSandraMuellerBarbara (2006), “The GLOBE Study — Applicability of a New Typology of Cultural Dimensions for Cross-Cultural Marketing and Advertising Research,” in International Advertising and Communication, DiehlSandra and
96.
ThakurVishesh (2023), “Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and Addressing Sociological Implications,” arXiv, https://doi.org/10.48550/arXiv.2307.09162.
97.
TungRosalie L.StahlGünter K. (2018), “The Tortuous Evolution of the Role of Culture in IB Research: What We Know, What We Don’t Know, and Where We Are Headed,” Journal of International Business Studies, 49 (9), 1167–89.
98.
TungRosalie L.VerbekeAlain (2010), “Beyond Hofstede and GLOBE: Improving the Quality of Cross-Cultural Research,” Journal of International Business Studies, 41 (8), 1259–74.
99.
TyrväinenOlliKarjaluotoHeikkiSaarijärviHannu (2020), “Personalization and Hedonic Motivation in Creating Customer Experiences and Loyalty in Omnichannel Retail,” Journal of Retailing and Consumer Services, 57, 102233.
100.
UnglessEddie L.RaffertyAmyNagHrichikaRossBjörn (2022), “A Robust Bias Mitigation Procedure Based on the Stereotype Content Model,” in Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP + CSS), BammanDavidHovyDirkJurgensDavidKeithKatherineO’ConnorBrendanVolkovaSvitlana, eds. Association for Computational Linguistics, 207–17.
101.
VaswaniAshishShazeerNoamParmarNikiUszkoreitJakobJonesLlionGomezAidan N.KaiserŁukaszPolosukhinIllia (2017), “Attention Is All You Need,” in Advances in Neural Information Processing Systems, Proceedings of the 31st International Conference on Neural Information Processing Systems, 5999–6009, https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.
102.
VenaikSunilBrewerPaul (2010), “Avoiding Uncertainty in Hofstede and GLOBE,” Journal of International Business Studies, 41 (8), 1294–312.
WangYufeiZhongWanjunLiLiangyouMiFeiZengXingshanHuangWenyongShangLifengJiangXinLiuQun (2023), “Aligning Large Language Models with Human: A Survey,” arXiv, https://doi.org/10.48550/arXiv.2307.12966.
105.
WebsterKellieWangXuezhiTenneyIanBeutelAlexPitlerEmilyPavlickEllieChenJilinChiEdPetrovSlav (2020), “Measuring and Reducing Gendered Correlations in Pre-Trained Models,” arXiv, https://doi.org/10.48550/arXiv.2010.06032.
106.
WeidingerLauraMellorJohnRauhMaribethGriffinConorUesatoJonathanHuangPo-SenChengMyraGlaeseMiaBalleBorjaKasirzadehAtoosaKentonZacBrownSashaHawkinsWillStepletonTomBilesCourtneyBirhaneAbebaHaasJuliaRimellLauraHendricksLisa AnneIsaacWilliam, et al. (2021), “Ethical and Social Risks of Harm from Language Models,” arXiv, https://doi.org/10.48550/arXiv.2112.04359.
107.
WolfeRobertYangYiweiHoweBillCaliskanAylin (2023), “Contrastive Language-Vision AI Models Pretrained on Web-Scraped Multimodal Data Exhibit Sexual Objectification Bias,” FAccT ’23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, 1174–85.
YeganehHamid (2014), “A Weighted, Mahalanobian, and Asymmetrical Approach to Calculating National Cultural Distance,” Journal of International Management, 20 (4), 436–63.
ZeißlerJenniferMandlerTimoKimJeeyeon (2023), “What Drives Royalty Rates in International Franchising?” Journal of International Marketing, 31 (2), 103–23.
115.
ZhangHanGoodfellowIanMetaxasDimitrisOdenaAugustus (2019), “Self-Attention Generative Adversarial Networks,” in Proceedings of the 36th International Conference on Machine Learning. PMLR, 97, 7354–63.
116.
ZmigrodRanMielkeSebastian J.WallachHannaCotterellRyan (2020), “Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, KorhonenAnnaTraumDavid, and Lluís Màrquez, eds. Association for Computational Linguistics, 1651–61.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
